The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is
the topic of recent studies in the geosciences, in which the key output is a surrogate model …

Abstract Model error is one of the main obstacles to improved accuracy and reliability in
numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art …

This paper describes the new global Navy Earth System Prediction Capability (Navy‐ESPC)
coupled atmosphere‐ocean‐sea ice prediction system developed at the Naval Research …

Data assimilation is often viewed as a framework for correcting short‐term error growth in
dynamical climate model forecasts. When viewed on the time scales of climate however …

Recent studies have demonstrated that it is possible to combine machine learning with data
assimilation to reconstruct the dynamics of a physical model partially and imperfectly …

Abstract The Met Office Global and Regional Ensemble Prediction System–Global
(MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial …

Weather forecasts made with imperfect models contain state‐dependent errors. Data
assimilation (DA) partially corrects these errors with new information from observations. As …

In numerical weather prediction models, near‐surface quantities like 10‐m wind speed
(FF10M) or 2‐m temperature (T2M) tend to exhibit significantly larger forecast errors than the …

We develop and compare model‐error representation schemes derived from data
assimilation increments and nudging tendencies in multidecadal simulations of the …

Abstract The National Oceanic and Atmospheric Administration has developed a very high-
resolution streamflow forecast using National Water Model (NWM) for 2.7 million stream …